1. Field of the Invention
The present invention relates to an optical information recording/reproducing apparatus adapted to record/reproduce multilevel information on/from an information recording medium such as an optical disk. More particularly, the present invention relates to a multilevel data processing apparatus adapted to easily process multilevel data subjected to trellis-coded modulation.
2. Description of the Related Art
In a conventional optical disk, binary digital data is recorded on a track formed in a spiral shape or tracks in the form of concentric circles by forming recessed or embossed pits (in the case of ROM disks), by forming pits in an organic/inorganic recording film (in the case of write-once read-many optical disks), or by changing the crystal state (in the case of phase-changing disks). To reproduce such data, a track is illuminated with a laser beam, and a reproduced RF signal is obtained by detecting a variation in strength of reflected light or a variation in polarization direction due to the magnetic Kerr effect. The binary data is then detected from the reproduced RF signal.
In recent years, research and development has been performed with the objective of achieving a greater recording density for data recorded on an optical disk. One technique for increasing the recording density is to reduce the size of a light spot used to record/reproduce information. To this end, a recent trend is to use blue-violet light (with a wavelength of 405 nm) rather than red light (with a wavelength of 650 nm). Another trend is to increase the numeric aperture of an objective lens from 0.6 to 0.85. Another technique for increasing the recording density without reducing the light spot size is to represent data in a more efficient manner using a multilevel representation technique.
Japanese Unexamined Patent Application Publication No. 5-128530, filed by the same applicant as the applicant for the present invention, discloses a technique to record/reproduce multilevel information.
In this technique, when multilevel information is recorded, the value of each pit recorded on a track of on an optical information recording medium is represented by a combination of the length of each pit as measured along the track and a shift of the pit in the direction along the track with reference to the light spot used in reproduction. The multilevel information recorded in the form of information pits is reproduced by comparing the signal detected by the light spot with learning data of signals.
In a technique disclosed in a presentation (“Writeonce Disks for Multi-level Optical Recording”, Fr-Po-04) at ISOM 2003, recording/reproducing of multilevel (8-level) data was achieved by forming virtual regions (called cells) on an optical disk with a track pitch of 0.46 μm and writing/reading data in/from cells via an optical system including a light source having a numeric aperture (NA) of 0.65 and including a light source configured to emit blue-violet light (with a wavelength of 405 nm).
When data is recorded on the information recording medium, original binary data is converted into 8-level data and resultant 8-level data is recorded. That is, 8-level data corresponding to 3-bit binary data is recorded in one cell.
For example, 3-bit information is defined as follows: (0, 0, 0) indicates level “0”; (0, 0, 1) indicates level “1”; (0, 1, 0) indicates level “2”; (0, 1, 1) indicates level “3”; (1, 1, 0) indicates level “4”; (1, 1, 1) indicates level “5”; (1, 0, 0) indicates level “6”; and (1, 0, 1) indicates level “7”.
Each of the above-described eight levels is represented by forming a pit to have a length equal to a selected integral number times one-sixteenth of the total cell length in the track direction, as shown in FIG. 1. That is, level “0” is represented by an information pit with a length of 0, level “1” is represented by an information pit with a length of 2 times one-sixteenth of the total cell length, level “2” is represented by an information pit with a length of 4 times one-sixteenth of the total cell length, level “3” is represented by an information pit with a length of 6 times one-sixteenth of the total cell length, level “4” is represented by an information pit with a length of 8 times one-sixteenth of the total cell length, level “5” is represented by an information pit with a length of 10 times one-sixteenth of the total cell length, level “6” is represented by an information pit with a length of 12 times one-sixteenth of the total cell length, and level “7” is represented by an information pit with a length of 14 times one-sixteenth of the total cell length.
When information pits representing various levels in the above-described manner are recorded at random and light reflected from these information pits is detected by a photodetector, the amplitude of the reproduced signal detected from the multilevel information pits has a distribution, for example, such as that shown in FIG. 2.
Sampling is performed when the center of the light spot comes to the center of the length of a cell in the direction along the track.
Note that values of respective levels are normalized such that the output signal level becomes “1” for the reproduced signal obtained for a sequence of a plurality of information pits with data level of “0” (no information is written), and the output signal level becomes “0” for a sequence of a plurality of information pits with data level of “7”.
The reason the magnitude of each level of the reproduced signal is scattered over a particular range is that the magnitude of a signal detected from an information pit of interest is influenced by preceding and following information pits; that is, intersymbol interference occurs.
If the amplitude distribution of each level of the reproduced signal overlaps the distribution of an adjacent level, as in the example shown in FIG. 2, it can be impossible to completely distinguish adjacent levels using fixed threshold values.
In the technique disclosed in ISOM 2003, learning is first performed. In the learning process, reproduced signals are read for a plurality of pit sequences each including three successive information pits having known values (i.e., a current pit of interest, the previous pit, and the following pit). The result is recorded as learning data.
When actual reproduced signals are read from information pits, the obtained reproduced signals are compared with the learning data (that is, correlation is checked) to correctly detect signal levels, which thereby can solve the above-described problem with intersymbol interference.
In addition to intersymbol interference, optical disks also have a problem in that a level variation or an amplitude variation can occur because of various factors such as a difference in reflectance among optical disks or a difference in reproduction frequency characteristics between inner tracks and outer tracks on the same optical disk. Such a level variation can cause an error in detection of the level of a reproduced signal even when the detection is performed using learning data according to the above-described technique. Japanese Patent No. 3475627 discloses a reproducing apparatus capable of correctly reproducing data even when both random noise and signal distortion such as intersymbol interference exist.
In the reproducing apparatus, levels of multilevel data subjected to trellis coded modulation are tentatively determined by a plurality of tentative data level detection units, each of which is configured to tentatively determine a particular part of the multilevel data. Reproduced data values are estimated on the basis of the tentatively determined data values, and decoding is performed by determining distances from reference values.
FIG. 3 is a block diagram showing a decoder according to the above technique. An input signal is supplied to 8 2-dimensional decoders 301-1 to 301-8 and recorded data are tentatively decoded. On the basis of tentative values of the data output from the 2-dimensional decoders 301-1 to 301-8, value prediction units 302-1 to 302-8 estimate correct reproduced data, and adders 303-1 to 303-8 calculate distances between the actual reproduced data values and the estimated correct values. Square circuits 304-1 to 304-8 determine error power by squaring the distances (differences) and supply the result to a Viterbi decoder 305, which decodes the multilevel data. This technique is said to be capable of achieving high decoding performance even when the reproduced signal includes random noise or signal distortion or both.
Thus, in the above-described technique, the trellis coded modulation is applied to part of multilevel data, the part of data is tentatively decoded by the plurality of tentative data level detection units, and the final decoded data is determined by calculating the distance between the data estimated on the basis of the tentative values and the reference values. However, the above-described technique has the following problems.
In the technique described above, the signal input to the decoder includes two symbols, and each tentative data level detection unit includes 8 2-dimensional decoders. Each 2-dimensional decoder has 8 reference values, and thus there are a total of 64 reference values. In the case of an input signal including 4 symbols, that is, in the case of a 4-dimensional signal, the tentative data level detection unit is configured to include a plurality of 4-dimensional decoders, and 84 or 4096 reference values are needed to decode 8-level data. That is, the necessary number of reference values increases exponentially with the dimension.
Thus, in the case of a reproducing apparatus having a tentative data level detection unit including m-dimensional decoders for decoding n-level multilevel data, as many as nm learning tables are needed for use by the value prediction units 302. To obtain high decoding performance, it is necessary to increase m. However, this results in an increase in the integration degree of a memory integrated circuit. Another problem is that when learning data stored in the memory integrated circuit is updated while reproducing random data, there are a small number of applicable data, and thus updating of the learning data is not performed frequently, which can result in a reduction in decoding performance. When test data is used to update the learning data, the learning can require a long time.